Investigating Turf Burn–Related Videos on TikTok: Cross-sectional Study
Notice bibliographique
Résumé
BACKGROUND: Due to the increased use of artificial turf, turf burn has become a common sports injury. Turf burn is caused by exposed skin sliding on artificial turf. Health complications, such as methicillin-resistant Staphylococcus aureus outbreaks, sepsis, and pneumonia, have been linked to untreated turf burns, and many athletes have been turning to social media for advice and companionship regarding their sports injuries. OBJECTIVE: The goal of this study is to categorize and quantitatively assess the percentage of turf burn-related posts on TikTok based on creator type, content, athletes' experiences, and treatment and prevention methods. With these data, we not only investigate if there is room for health care professionals to assist in the distribution of evidence-based health education to athletes to counteract misinformation but also investigate if there is a potential audience of athletes on TikTok who have the potential to develop problematic responses to injuries. METHODS: By using the Discover page on TikTok, we searched for the term turf burn on October 17, 2021. In total, 100 videos were analyzed. Videos were categorized and analyzed based on creator type, content, experiences of the athletes, and treatment and prevention methods. The number of likes and comments was recorded. RESULTS: Most videos (98/100, 98%) were created by athletes. A small number of videos (2/100, 2%) were created by health care professionals. In terms of content, most videos (67/100, 67%) displayed turf burns. A small amount of videos (15/100, 15%) showed the incidents when turf burns were acquired, while around one-quarter of the videos (23/100, 23%) demonstrated the treatment and prevention of turf burns. Of the 23 treatment and prevention videos, a minority (4/23, 17%) showed the preferred treatment of turf burns, while most videos (19/23, 83%) showed nonpreferred treatments. The smallest amount of videos (2/100, 2%) were about turf burn education. Most of the videos created by athletes (56/98, 57%) depicted the negative experiences that patients had with turf burns. Some videos (37/98, 38%) depicted neutral experiences, while the smallest amount of videos (5/98, 5%) depicted positive experiences. CONCLUSIONS: Our study suggests that there is a potential audience of athletes on TikTok who could develop problematic responses to sports injuries, such as turf burns, as most of the people who post videos are athletes, and many of the posts demonstrate negative experiences associated with turf burns. TikTok is a growing social media platform that should be studied to determine if it can be used to create a social support group for injured athletes to prevent the progression of negative emotional responses into problematic responses. Physicians should also have a role in establishing their social media presence on TikTok and offering evidence-based advice to athletes while disproving misinformation on TikTok.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,003 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».